{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check run for errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import re\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "from IPython.display import display, Markdown, Latex\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 8)\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-10-11_optim-part4'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-12-02_runtime-benchmark-2-merged'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/2021-11-01_runtime-gutentag-merged'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-14_optim-extra'),\n",
       " PosixPath('/home/projects/akita/results/2021-11-30_runtime-gutentag-2'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-08_optim-part3'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-17_optim-extra2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part5'),\n",
       " PosixPath('/home/projects/akita/results/backup'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part6'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-21_runtime-benchmark-fixed')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-11-30_runtime-gutentag-2\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\" #\"benchmark-data\" / \"data-processed\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-11-30_runtime-gutentag-2\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-11-30_runtime-gutentag-2\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# aggregate runtime\n",
    "df[\"overall_time\"] = df[\"execute_main_time\"].fillna(0) + df[\"train_main_time\"].fillna(0)\n",
    "\n",
    "# add RANGE_PR_AUC if it is not part of the results\n",
    "if \"RANGE_PR_AUC\" not in df.columns:\n",
    "    df[\"RANGE_PR_AUC\"] = np.nan\n",
    "\n",
    "# remove all duplicates (not necessary, but sometimes, we have some)\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "df[\"dataset\"] = df[\"dataset\"].astype(str)\n",
    "#df[\"dataset\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_id, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if not isinstance(algorithm_name, list):\n",
    "        algorithms = [algorithm_name]\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # deconstruct dataset ID\n",
    "    collection_name, dataset_name = dataset_id\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[(df[\"collection\"] == collection_name) & (df[\"dataset\"] == dataset_name), \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo in algorithms:\n",
    "        algos.append(algo)\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[algo] = df.loc[(df[\"algorithm\"] == algo) & (df[\"collection\"] == collection_name) & (df[\"dataset\"] == dataset_name), \"ROC_AUC\"].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} was not executed on {dataset_id}.\")\n",
    "            auroc[algo] = -1\n",
    "            skip_algos.append(algo)\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[algo] = load_scores_df(algo, dataset_id).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_id}.\")\n",
    "            df_scores[algo] = np.nan\n",
    "            skip_algos.append(algo)\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=df_dataset.columns[i]), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for algo in algorithms:\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[algo], name=f\"{algo}={auroc[algo]:.4f}\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique(algorithms))} on {dataset_id}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_id, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=df_dataset.columns[i])\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for algo in algorithms:\n",
    "        axs[1].plot(df_scores.index, df_scores[algo], label=f\"{algo}={auroc[algo]:.4f}\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique(algorithms))} on {dataset_id}\")\n",
    "    fig.tight_layout()\n",
    "    return fig\n",
    "\n",
    "def plot_boxplot(df, n_show = 20, title=\"Box plots\", ax_label=\"values\", fmt_label=lambda x: x, use_plotly=default_use_plotly):\n",
    "    n_show = n_show // 2\n",
    "    title = title + f\" (worst {n_show} and best {n_show} algorithms)\"\n",
    "    \n",
    "    if use_plotly:\n",
    "        import plotly.offline as py\n",
    "        import plotly.graph_objects as go\n",
    "        import plotly.figure_factory as ff\n",
    "        import plotly.express as px\n",
    "        from plotly.subplots import make_subplots\n",
    "        \n",
    "        fig = go.Figure()\n",
    "        for i, c in enumerate(df.columns):\n",
    "            fig.add_trace(go.Box(\n",
    "                x=df[c],\n",
    "                name=fmt_label(c),\n",
    "                boxpoints=False,\n",
    "                visible=None if i < n_show or i > len(df.columns)-n_show-1 else \"legendonly\"\n",
    "            ))\n",
    "        fig.update_layout(\n",
    "            title={\"text\": title, \"xanchor\": \"center\", \"x\": 0.5},\n",
    "            xaxis_title=ax_label,\n",
    "            legend_title=\"Algorithms\"\n",
    "        )\n",
    "        return py.iplot(fig)\n",
    "    else:\n",
    "        df_boxplot = pd.concat([df.iloc[:, :n_show], df.iloc[:, -n_show:]])\n",
    "        labels = df_boxplot.columns\n",
    "        labels = [fmt_label(c) for c in labels]\n",
    "        values = [df_boxplot[c].dropna().values for c in df_boxplot.columns]\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        #ax.boxplot(values, sym=\"\", vert=True, meanline=True, showmeans=True, showfliers=False, manage_ticks=True)\n",
    "        ax.boxplot(values, vert=True, meanline=True, showmeans=True, showfliers=True, manage_ticks=True)\n",
    "        ax.set_ylabel(ax_label)\n",
    "        ax.set_title(title)\n",
    "        ax.set_xticklabels(labels, rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "        # add vline to separate bad and good algos\n",
    "        ymin, ymax = ax.get_ylim()\n",
    "        ax.vlines([n_show + 0.5], ymin, ymax, colors=\"black\", linestyles=\"dashed\")\n",
    "        fig.tight_layout()\n",
    "        return fig\n",
    "\n",
    "def plot_algorithm_bars(df, y_name=\"ROC_AUC\", title=\"Bar chart for algorithms\", use_plotly=default_use_plotly):\n",
    "    if use_plotly:\n",
    "        fig = px.bar(df, x=\"algorithm\", y=y_name)\n",
    "        py.iplot(fig)\n",
    "    else:\n",
    "        fig = plt.figure()\n",
    "        ax = fig.gca()\n",
    "        ax.bar(df[\"algorithm\"], df[y_name], label=y_name)\n",
    "        ax.set_ylabel(y_name)\n",
    "        ax.set_title(title)\n",
    "        ax.set_xticklabels(df[\"algorithm\"], rotation=-45, ha=\"left\", rotation_mode=\"anchor\")\n",
    "        ax.legend()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze overall results on the GutenTAG datasets\n",
    "\n",
    "### Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>collection</th>\n",
       "      <th>dataset</th>\n",
       "      <th>status</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>AVERAGE_PRECISION</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>RANGE_PR_AUC</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>hyper_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-1.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.815319</td>\n",
       "      <td>0.454742</td>\n",
       "      <td>0.465248</td>\n",
       "      <td>0.453215</td>\n",
       "      <td>71.414111</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-2.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.480169</td>\n",
       "      <td>0.068871</td>\n",
       "      <td>0.061556</td>\n",
       "      <td>0.077541</td>\n",
       "      <td>134.413274</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-combined-diff-3.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.955978</td>\n",
       "      <td>0.241877</td>\n",
       "      <td>0.127965</td>\n",
       "      <td>0.136431</td>\n",
       "      <td>129.666755</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-diff-count-1.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.439091</td>\n",
       "      <td>0.014368</td>\n",
       "      <td>0.008516</td>\n",
       "      <td>0.016521</td>\n",
       "      <td>72.992341</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>cbf-diff-count-2.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.343218</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.007510</td>\n",
       "      <td>0.012396</td>\n",
       "      <td>106.307492</td>\n",
       "      <td>{\"differencing_degree\": 1, \"distance_metric\": ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10943</th>\n",
       "      <td>normal</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-pattern-shift.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10944</th>\n",
       "      <td>normal</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-pattern.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10945</th>\n",
       "      <td>normal</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-platform.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10946</th>\n",
       "      <td>normal</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-trend.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10947</th>\n",
       "      <td>normal</td>\n",
       "      <td>GutenTAG</td>\n",
       "      <td>sinus-type-variance.unsupervised</td>\n",
       "      <td>Status.OK</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10948 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      algorithm collection                                dataset     status  \\\n",
       "0         ARIMA   GutenTAG       cbf-combined-diff-1.unsupervised  Status.OK   \n",
       "1         ARIMA   GutenTAG       cbf-combined-diff-2.unsupervised  Status.OK   \n",
       "2         ARIMA   GutenTAG       cbf-combined-diff-3.unsupervised  Status.OK   \n",
       "3         ARIMA   GutenTAG          cbf-diff-count-1.unsupervised  Status.OK   \n",
       "4         ARIMA   GutenTAG          cbf-diff-count-2.unsupervised  Status.OK   \n",
       "...         ...        ...                                    ...        ...   \n",
       "10943    normal   GutenTAG  sinus-type-pattern-shift.unsupervised  Status.OK   \n",
       "10944    normal   GutenTAG        sinus-type-pattern.unsupervised  Status.OK   \n",
       "10945    normal   GutenTAG       sinus-type-platform.unsupervised  Status.OK   \n",
       "10946    normal   GutenTAG          sinus-type-trend.unsupervised  Status.OK   \n",
       "10947    normal   GutenTAG       sinus-type-variance.unsupervised  Status.OK   \n",
       "\n",
       "        ROC_AUC  AVERAGE_PRECISION    PR_AUC  RANGE_PR_AUC  execute_main_time  \\\n",
       "0      0.815319           0.454742  0.465248      0.453215          71.414111   \n",
       "1      0.480169           0.068871  0.061556      0.077541         134.413274   \n",
       "2      0.955978           0.241877  0.127965      0.136431         129.666755   \n",
       "3      0.439091           0.014368  0.008516      0.016521          72.992341   \n",
       "4      0.343218           0.011311  0.007510      0.012396         106.307492   \n",
       "...         ...                ...       ...           ...                ...   \n",
       "10943  0.500000           0.010000  0.505000      0.505000           0.000021   \n",
       "10944  0.500000           0.010000  0.505000      0.505000           0.000023   \n",
       "10945  0.500000           0.010000  0.505000      0.505000           0.000015   \n",
       "10946  0.500000           0.010000  0.505000      0.505000           0.000055   \n",
       "10947  0.500000           0.010000  0.505000      0.505000           0.000022   \n",
       "\n",
       "                                            hyper_params  \n",
       "0      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "1      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "2      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "3      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "4      {\"differencing_degree\": 1, \"distance_metric\": ...  \n",
       "...                                                  ...  \n",
       "10943                                                 {}  \n",
       "10944                                                 {}  \n",
       "10945                                                 {}  \n",
       "10946                                                 {}  \n",
       "10947                                                 {}  \n",
       "\n",
       "[10948 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"algorithm\", \"collection\", \"dataset\", \"status\", \"ROC_AUC\", \"AVERAGE_PRECISION\", \"PR_AUC\", \"RANGE_PR_AUC\", \"execute_main_time\", \"hyper_params\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_scores([\"MedianMethod\", \"Subsequence LOF\"], (\"GutenTAG\", \"ecg-diff-count-5.unsupervised\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_scores([\"LSTM-AD\"], (\"GutenTAG\", \"ecg-diff-count-5.semi-supervised\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"distance_metric_order\": 2, \"leaf_size\": 20, \"n_jobs\": 1, \"n_neighbors\": 50, \"random_state\": 42, \"window_size\": 20}'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df[\"algorithm\"] == \"Subsequence LOF\") & (df[\"dataset\"] == \"ecg-diff-count-5.unsupervised\"), \"hyper_params\"].item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Algorithm problems grouped by algorithm training type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEMI_SUPERVISED\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>Status.ERROR</th>\n",
       "      <th>Status.OK</th>\n",
       "      <th>Status.TIMEOUT</th>\n",
       "      <th>ALL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algo_input_dimensionality</th>\n",
       "      <th>algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">UNIVARIATE</th>\n",
       "      <th>TARZAN</th>\n",
       "      <td>51</td>\n",
       "      <td>253</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OceanWNN</th>\n",
       "      <td>46</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Donut</th>\n",
       "      <td>13</td>\n",
       "      <td>287</td>\n",
       "      <td>4</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bagel</th>\n",
       "      <td>9</td>\n",
       "      <td>208</td>\n",
       "      <td>87</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR-CNN</th>\n",
       "      <td>7</td>\n",
       "      <td>194</td>\n",
       "      <td>103</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ImageEmbeddingCAE</th>\n",
       "      <td>6</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoosting (RR)</th>\n",
       "      <td>3</td>\n",
       "      <td>301</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest Regressor (RR)</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>54</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">MULTIVARIATE</th>\n",
       "      <th>DeepAnT</th>\n",
       "      <td>379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTM-AD</th>\n",
       "      <td>226</td>\n",
       "      <td>87</td>\n",
       "      <td>66</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EncDec-AD</th>\n",
       "      <td>113</td>\n",
       "      <td>66</td>\n",
       "      <td>200</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Black Forest (RR)</th>\n",
       "      <td>75</td>\n",
       "      <td>284</td>\n",
       "      <td>20</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAnoGan</th>\n",
       "      <td>52</td>\n",
       "      <td>106</td>\n",
       "      <td>221</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telemanom</th>\n",
       "      <td>51</td>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OmniAnomaly</th>\n",
       "      <td>27</td>\n",
       "      <td>352</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LaserDBN</th>\n",
       "      <td>17</td>\n",
       "      <td>288</td>\n",
       "      <td>74</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hybrid KNN</th>\n",
       "      <td>12</td>\n",
       "      <td>319</td>\n",
       "      <td>48</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HealthESN</th>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>349</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RobustPCA</th>\n",
       "      <td>0</td>\n",
       "      <td>331</td>\n",
       "      <td>48</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status                                                  Status.ERROR  \\\n",
       "algo_input_dimensionality algorithm                                    \n",
       "UNIVARIATE                TARZAN                                  51   \n",
       "                          OceanWNN                                46   \n",
       "                          Donut                                   13   \n",
       "                          Bagel                                    9   \n",
       "                          SR-CNN                                   7   \n",
       "                          ImageEmbeddingCAE                        6   \n",
       "                          XGBoosting (RR)                          3   \n",
       "                          Random Forest Regressor (RR)             0   \n",
       "MULTIVARIATE              DeepAnT                                379   \n",
       "                          LSTM-AD                                226   \n",
       "                          EncDec-AD                              113   \n",
       "                          Random Black Forest (RR)                75   \n",
       "                          TAnoGan                                 52   \n",
       "                          Telemanom                               51   \n",
       "                          OmniAnomaly                             27   \n",
       "                          LaserDBN                                17   \n",
       "                          Hybrid KNN                              12   \n",
       "                          HealthESN                                7   \n",
       "                          RobustPCA                                0   \n",
       "\n",
       "status                                                  Status.OK  \\\n",
       "algo_input_dimensionality algorithm                                 \n",
       "UNIVARIATE                TARZAN                              253   \n",
       "                          OceanWNN                            258   \n",
       "                          Donut                               287   \n",
       "                          Bagel                               208   \n",
       "                          SR-CNN                              194   \n",
       "                          ImageEmbeddingCAE                   298   \n",
       "                          XGBoosting (RR)                     301   \n",
       "                          Random Forest Regressor (RR)        250   \n",
       "MULTIVARIATE              DeepAnT                               0   \n",
       "                          LSTM-AD                              87   \n",
       "                          EncDec-AD                            66   \n",
       "                          Random Black Forest (RR)            284   \n",
       "                          TAnoGan                             106   \n",
       "                          Telemanom                           327   \n",
       "                          OmniAnomaly                         352   \n",
       "                          LaserDBN                            288   \n",
       "                          Hybrid KNN                          319   \n",
       "                          HealthESN                            23   \n",
       "                          RobustPCA                           331   \n",
       "\n",
       "status                                                  Status.TIMEOUT  ALL  \n",
       "algo_input_dimensionality algorithm                                          \n",
       "UNIVARIATE                TARZAN                                     0  304  \n",
       "                          OceanWNN                                   0  304  \n",
       "                          Donut                                      4  304  \n",
       "                          Bagel                                     87  304  \n",
       "                          SR-CNN                                   103  304  \n",
       "                          ImageEmbeddingCAE                          0  304  \n",
       "                          XGBoosting (RR)                            0  304  \n",
       "                          Random Forest Regressor (RR)              54  304  \n",
       "MULTIVARIATE              DeepAnT                                    0  379  \n",
       "                          LSTM-AD                                   66  379  \n",
       "                          EncDec-AD                                200  379  \n",
       "                          Random Black Forest (RR)                  20  379  \n",
       "                          TAnoGan                                  221  379  \n",
       "                          Telemanom                                  1  379  \n",
       "                          OmniAnomaly                                0  379  \n",
       "                          LaserDBN                                  74  379  \n",
       "                          Hybrid KNN                                48  379  \n",
       "                          HealthESN                                349  379  \n",
       "                          RobustPCA                                 48  379  "
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       "status                                                   Status.ERROR  \\\n",
       "algo_input_dimensionality algorithm                                     \n",
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       "\n",
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       "algo_input_dimensionality algorithm                                           \n",
       "MULTIVARIATE              MultiHMM                                    0    6  \n",
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       "                          Hybrid Isolation Forest (HIF)               1    6  "
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">UNIVARIATE</th>\n",
       "      <th>S-H-ESD (Twitter)</th>\n",
       "      <td>444</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
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       "      <th>SAND</th>\n",
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       "      <th>VALMOD</th>\n",
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       "      <td>738</td>\n",
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       "      <th>Triple ES (Holt-Winter's)</th>\n",
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       "      <th>Series2Graph</th>\n",
       "      <td>45</td>\n",
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       "      <td>738</td>\n",
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       "      <th>NormA</th>\n",
       "      <td>39</td>\n",
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       "      <td>78</td>\n",
       "      <td>738</td>\n",
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       "      <th>PST</th>\n",
       "      <td>33</td>\n",
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       "      <td>738</td>\n",
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       "      <th>HOT SAX</th>\n",
       "      <td>19</td>\n",
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       "      <td>738</td>\n",
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       "      <th>Left STAMPi</th>\n",
       "      <td>12</td>\n",
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       "      <td>738</td>\n",
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       "      <th>ARIMA</th>\n",
       "      <td>4</td>\n",
       "      <td>676</td>\n",
       "      <td>58</td>\n",
       "      <td>738</td>\n",
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       "      <th>PhaseSpace-SVM</th>\n",
       "      <td>3</td>\n",
       "      <td>626</td>\n",
       "      <td>109</td>\n",
       "      <td>738</td>\n",
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       "      <th>MedianMethod</th>\n",
       "      <td>1</td>\n",
       "      <td>737</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
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       "      <th>NumentaHTM</th>\n",
       "      <td>1</td>\n",
       "      <td>733</td>\n",
       "      <td>4</td>\n",
       "      <td>738</td>\n",
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       "      <th>DSPOT</th>\n",
       "      <td>0</td>\n",
       "      <td>686</td>\n",
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       "      <td>738</td>\n",
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       "      <th>DWT-MLEAD</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFT</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrammarViz</th>\n",
       "      <td>0</td>\n",
       "      <td>711</td>\n",
       "      <td>27</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCI</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
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       "    <tr>\n",
       "      <th>SSA</th>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>5</td>\n",
       "      <td>738</td>\n",
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       "    <tr>\n",
       "      <th>STAMP</th>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>738</td>\n",
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       "      <th>STOMP</th>\n",
       "      <td>0</td>\n",
       "      <td>723</td>\n",
       "      <td>15</td>\n",
       "      <td>738</td>\n",
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       "      <th>Spectral Residual (SR)</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
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       "      <th>Subsequence IF</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
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       "    <tr>\n",
       "      <th>Subsequence LOF</th>\n",
       "      <td>0</td>\n",
       "      <td>720</td>\n",
       "      <td>18</td>\n",
       "      <td>738</td>\n",
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       "      <th>TSBitmap</th>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "      <td>738</td>\n",
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       "      <th rowspan=\"14\" valign=\"top\">MULTIVARIATE</th>\n",
       "      <th>DBStream</th>\n",
       "      <td>719</td>\n",
       "      <td>203</td>\n",
       "      <td>1</td>\n",
       "      <td>923</td>\n",
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       "      <th>COF</th>\n",
       "      <td>374</td>\n",
       "      <td>549</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k-Means</th>\n",
       "      <td>170</td>\n",
       "      <td>753</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normal</th>\n",
       "      <td>93</td>\n",
       "      <td>830</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IF-LOF</th>\n",
       "      <td>47</td>\n",
       "      <td>870</td>\n",
       "      <td>6</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CBLOF</th>\n",
       "      <td>23</td>\n",
       "      <td>900</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Torsk</th>\n",
       "      <td>23</td>\n",
       "      <td>737</td>\n",
       "      <td>163</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COPOD</th>\n",
       "      <td>20</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
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       "    <tr>\n",
       "      <th>HBOS</th>\n",
       "      <td>20</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Isolation Forest (iForest)</th>\n",
       "      <td>20</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>20</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOF</th>\n",
       "      <td>20</td>\n",
       "      <td>902</td>\n",
       "      <td>1</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCC</th>\n",
       "      <td>20</td>\n",
       "      <td>903</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extended Isolation Forest (EIF)</th>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
       "      <td>0</td>\n",
       "      <td>923</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "status                                                     Status.ERROR  \\\n",
       "algo_input_dimensionality algorithm                                       \n",
       "UNIVARIATE                S-H-ESD (Twitter)                         444   \n",
       "                          SAND                                      184   \n",
       "                          VALMOD                                    168   \n",
       "                          Triple ES (Holt-Winter's)                  78   \n",
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       "                          NormA                                      39   \n",
       "                          PST                                        33   \n",
       "                          HOT SAX                                    19   \n",
       "                          Left STAMPi                                12   \n",
       "                          ARIMA                                       4   \n",
       "                          PhaseSpace-SVM                              3   \n",
       "                          MedianMethod                                1   \n",
       "                          NumentaHTM                                  1   \n",
       "                          DSPOT                                       0   \n",
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       "                          GrammarViz                                  0   \n",
       "                          PCI                                         0   \n",
       "                          SSA                                         0   \n",
       "                          STAMP                                       0   \n",
       "                          STOMP                                       0   \n",
       "                          Spectral Residual (SR)                      0   \n",
       "                          Subsequence IF                              0   \n",
       "                          Subsequence LOF                             0   \n",
       "                          TSBitmap                                    0   \n",
       "MULTIVARIATE              DBStream                                  719   \n",
       "                          COF                                       374   \n",
       "                          k-Means                                   170   \n",
       "                          normal                                     93   \n",
       "                          IF-LOF                                     47   \n",
       "                          CBLOF                                      23   \n",
       "                          Torsk                                      23   \n",
       "                          COPOD                                      20   \n",
       "                          HBOS                                       20   \n",
       "                          Isolation Forest (iForest)                 20   \n",
       "                          KNN                                        20   \n",
       "                          LOF                                        20   \n",
       "                          PCC                                        20   \n",
       "                          Extended Isolation Forest (EIF)             0   \n",
       "\n",
       "status                                                     Status.OK  \\\n",
       "algo_input_dimensionality algorithm                                    \n",
       "UNIVARIATE                S-H-ESD (Twitter)                      294   \n",
       "                          SAND                                   508   \n",
       "                          VALMOD                                 558   \n",
       "                          Triple ES (Holt-Winter's)              525   \n",
       "                          Series2Graph                           693   \n",
       "                          NormA                                  621   \n",
       "                          PST                                    705   \n",
       "                          HOT SAX                                555   \n",
       "                          Left STAMPi                            710   \n",
       "                          ARIMA                                  676   \n",
       "                          PhaseSpace-SVM                         626   \n",
       "                          MedianMethod                           737   \n",
       "                          NumentaHTM                             733   \n",
       "                          DSPOT                                  686   \n",
       "                          DWT-MLEAD                              738   \n",
       "                          FFT                                    738   \n",
       "                          GrammarViz                             711   \n",
       "                          PCI                                    738   \n",
       "                          SSA                                    733   \n",
       "                          STAMP                                  699   \n",
       "                          STOMP                                  723   \n",
       "                          Spectral Residual (SR)                 738   \n",
       "                          Subsequence IF                         738   \n",
       "                          Subsequence LOF                        720   \n",
       "                          TSBitmap                               738   \n",
       "MULTIVARIATE              DBStream                               203   \n",
       "                          COF                                    549   \n",
       "                          k-Means                                753   \n",
       "                          normal                                 830   \n",
       "                          IF-LOF                                 870   \n",
       "                          CBLOF                                  900   \n",
       "                          Torsk                                  737   \n",
       "                          COPOD                                  903   \n",
       "                          HBOS                                   903   \n",
       "                          Isolation Forest (iForest)             903   \n",
       "                          KNN                                    903   \n",
       "                          LOF                                    902   \n",
       "                          PCC                                    903   \n",
       "                          Extended Isolation Forest (EIF)        923   \n",
       "\n",
       "status                                                     Status.TIMEOUT  ALL  \n",
       "algo_input_dimensionality algorithm                                             \n",
       "UNIVARIATE                S-H-ESD (Twitter)                             0  738  \n",
       "                          SAND                                         46  738  \n",
       "                          VALMOD                                       12  738  \n",
       "                          Triple ES (Holt-Winter's)                   135  738  \n",
       "                          Series2Graph                                  0  738  \n",
       "                          NormA                                        78  738  \n",
       "                          PST                                           0  738  \n",
       "                          HOT SAX                                     164  738  \n",
       "                          Left STAMPi                                  16  738  \n",
       "                          ARIMA                                        58  738  \n",
       "                          PhaseSpace-SVM                              109  738  \n",
       "                          MedianMethod                                  0  738  \n",
       "                          NumentaHTM                                    4  738  \n",
       "                          DSPOT                                        52  738  \n",
       "                          DWT-MLEAD                                     0  738  \n",
       "                          FFT                                           0  738  \n",
       "                          GrammarViz                                   27  738  \n",
       "                          PCI                                           0  738  \n",
       "                          SSA                                           5  738  \n",
       "                          STAMP                                        39  738  \n",
       "                          STOMP                                        15  738  \n",
       "                          Spectral Residual (SR)                        0  738  \n",
       "                          Subsequence IF                                0  738  \n",
       "                          Subsequence LOF                              18  738  \n",
       "                          TSBitmap                                      0  738  \n",
       "MULTIVARIATE              DBStream                                      1  923  \n",
       "                          COF                                           0  923  \n",
       "                          k-Means                                       0  923  \n",
       "                          normal                                        0  923  \n",
       "                          IF-LOF                                        6  923  \n",
       "                          CBLOF                                         0  923  \n",
       "                          Torsk                                       163  923  \n",
       "                          COPOD                                         0  923  \n",
       "                          HBOS                                          0  923  \n",
       "                          Isolation Forest (iForest)                    0  923  \n",
       "                          KNN                                           0  923  \n",
       "                          LOF                                           1  923  \n",
       "                          PCC                                           0  923  \n",
       "                          Extended Isolation Forest (EIF)               0  923  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index_columns = [\"algo_training_type\", \"algo_input_dimensionality\", \"algorithm\"]\n",
    "df_error_counts = df.pivot_table(index=index_columns, columns=[\"status\"], values=\"repetition\", aggfunc=\"count\")\n",
    "if \"Status.TIMEOUT\" not in df_error_counts.columns:\n",
    "    df_error_counts[\"Status.TIMEOUT\"] = np.nan\n",
    "df_error_counts = df_error_counts.fillna(value=0).astype(np.int64)\n",
    "df_error_counts = df_error_counts.reset_index().sort_values(by=[\"algo_input_dimensionality\", \"Status.ERROR\"], ascending=False).set_index(index_columns)\n",
    "df_error_counts[\"ALL\"] = df_error_counts[\"Status.ERROR\"] + df_error_counts[\"Status.OK\"] + df_error_counts[\"Status.TIMEOUT\"]\n",
    "\n",
    "for tpe in [\"SEMI_SUPERVISED\", \"SUPERVISED\", \"UNSUPERVISED\"]:\n",
    "    if tpe in df_error_counts.index:\n",
    "        print(tpe)\n",
    "        if default_use_plotly:\n",
    "            py.iplot(ff.create_table(df_error_counts.loc[tpe], index=True))\n",
    "        else:\n",
    "            display(df_error_counts.loc[tpe])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f3441_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >count</th>\n",
       "      <th class=\"col_heading level0 col1\" >percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >status</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f3441_level0_row0\" class=\"row_heading level0 row0\" >Status.ERROR</th>\n",
       "      <td id=\"T_f3441_row0_col0\" class=\"data row0 col0\" >3701</td>\n",
       "      <td id=\"T_f3441_row0_col1\" class=\"data row0 col1\" >09.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3441_level0_row1\" class=\"row_heading level0 row1\" >Status.OK</th>\n",
       "      <td id=\"T_f3441_row1_col0\" class=\"data row1 col0\" >32062</td>\n",
       "      <td id=\"T_f3441_row1_col1\" class=\"data row1 col1\" >84.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3441_level0_row2\" class=\"row_heading level0 row2\" >Status.TIMEOUT</th>\n",
       "      <td id=\"T_f3441_row2_col0\" class=\"data row2 col0\" >2228</td>\n",
       "      <td id=\"T_f3441_row2_col1\" class=\"data row2 col1\" >05.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f3441_level0_row3\" class=\"row_heading level0 row3\" >ALL</th>\n",
       "      <td id=\"T_f3441_row3_col0\" class=\"data row3 col0\" >37991</td>\n",
       "      <td id=\"T_f3441_row3_col1\" class=\"data row3 col1\" >100.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1d12fe2760>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error_summary = pd.DataFrame(df_error_counts.sum(axis=0))\n",
    "df_error_summary.columns = [\"count\"]\n",
    "all_count = df_error_summary.loc[\"ALL\", \"count\"]\n",
    "df_error_summary[\"percentage\"] = df_error_summary / all_count\n",
    "df_error_summary.style.format({\"percentage\": \"{:06.2%}\".format})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect errors of a specific algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_aeb61_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >algorithm</th>\n",
       "      <th class=\"col_heading level0 col0\" >ALL (sum)</th>\n",
       "      <th class=\"col_heading level0 col1\" >ARIMA</th>\n",
       "      <th class=\"col_heading level0 col2\" >Bagel</th>\n",
       "      <th class=\"col_heading level0 col3\" >CBLOF</th>\n",
       "      <th class=\"col_heading level0 col4\" >COF</th>\n",
       "      <th class=\"col_heading level0 col5\" >COPOD</th>\n",
       "      <th class=\"col_heading level0 col6\" >DBStream</th>\n",
       "      <th class=\"col_heading level0 col7\" >DSPOT</th>\n",
       "      <th class=\"col_heading level0 col8\" >DWT-MLEAD</th>\n",
       "      <th class=\"col_heading level0 col9\" >DeepAnT</th>\n",
       "      <th class=\"col_heading level0 col10\" >Donut</th>\n",
       "      <th class=\"col_heading level0 col11\" >EncDec-AD</th>\n",
       "      <th class=\"col_heading level0 col12\" >Extended Isolation Forest (EIF)</th>\n",
       "      <th class=\"col_heading level0 col13\" >FFT</th>\n",
       "      <th class=\"col_heading level0 col14\" >GrammarViz</th>\n",
       "      <th class=\"col_heading level0 col15\" >HBOS</th>\n",
       "      <th class=\"col_heading level0 col16\" >HOT SAX</th>\n",
       "      <th class=\"col_heading level0 col17\" >HealthESN</th>\n",
       "      <th class=\"col_heading level0 col18\" >Hybrid Isolation Forest (HIF)</th>\n",
       "      <th class=\"col_heading level0 col19\" >Hybrid KNN</th>\n",
       "      <th class=\"col_heading level0 col20\" >IF-LOF</th>\n",
       "      <th class=\"col_heading level0 col21\" >ImageEmbeddingCAE</th>\n",
       "      <th class=\"col_heading level0 col22\" >Isolation Forest (iForest)</th>\n",
       "      <th class=\"col_heading level0 col23\" >KNN</th>\n",
       "      <th class=\"col_heading level0 col24\" >LOF</th>\n",
       "      <th class=\"col_heading level0 col25\" >LSTM-AD</th>\n",
       "      <th class=\"col_heading level0 col26\" >LaserDBN</th>\n",
       "      <th class=\"col_heading level0 col27\" >Left STAMPi</th>\n",
       "      <th class=\"col_heading level0 col28\" >MedianMethod</th>\n",
       "      <th class=\"col_heading level0 col29\" >MultiHMM</th>\n",
       "      <th class=\"col_heading level0 col30\" >NormA</th>\n",
       "      <th class=\"col_heading level0 col31\" >Normalizing Flows</th>\n",
       "      <th class=\"col_heading level0 col32\" >NumentaHTM</th>\n",
       "      <th class=\"col_heading level0 col33\" >OceanWNN</th>\n",
       "      <th class=\"col_heading level0 col34\" >OmniAnomaly</th>\n",
       "      <th class=\"col_heading level0 col35\" >PCC</th>\n",
       "      <th class=\"col_heading level0 col36\" >PCI</th>\n",
       "      <th class=\"col_heading level0 col37\" >PST</th>\n",
       "      <th class=\"col_heading level0 col38\" >PhaseSpace-SVM</th>\n",
       "      <th class=\"col_heading level0 col39\" >Random Black Forest (RR)</th>\n",
       "      <th class=\"col_heading level0 col40\" >Random Forest Regressor (RR)</th>\n",
       "      <th class=\"col_heading level0 col41\" >RobustPCA</th>\n",
       "      <th class=\"col_heading level0 col42\" >S-H-ESD (Twitter)</th>\n",
       "      <th class=\"col_heading level0 col43\" >SAND</th>\n",
       "      <th class=\"col_heading level0 col44\" >SR-CNN</th>\n",
       "      <th class=\"col_heading level0 col45\" >SSA</th>\n",
       "      <th class=\"col_heading level0 col46\" >STAMP</th>\n",
       "      <th class=\"col_heading level0 col47\" >STOMP</th>\n",
       "      <th class=\"col_heading level0 col48\" >Series2Graph</th>\n",
       "      <th class=\"col_heading level0 col49\" >Spectral Residual (SR)</th>\n",
       "      <th class=\"col_heading level0 col50\" >Subsequence IF</th>\n",
       "      <th class=\"col_heading level0 col51\" >Subsequence LOF</th>\n",
       "      <th class=\"col_heading level0 col52\" >TARZAN</th>\n",
       "      <th class=\"col_heading level0 col53\" >TAnoGan</th>\n",
       "      <th class=\"col_heading level0 col54\" >TSBitmap</th>\n",
       "      <th class=\"col_heading level0 col55\" >Telemanom</th>\n",
       "      <th class=\"col_heading level0 col56\" >Torsk</th>\n",
       "      <th class=\"col_heading level0 col57\" >Triple ES (Holt-Winter's)</th>\n",
       "      <th class=\"col_heading level0 col58\" >VALMOD</th>\n",
       "      <th class=\"col_heading level0 col59\" >XGBoosting (RR)</th>\n",
       "      <th class=\"col_heading level0 col60\" >k-Means</th>\n",
       "      <th class=\"col_heading level0 col61\" >normal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >error_category</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "      <th class=\"blank col21\" >&nbsp;</th>\n",
       "      <th class=\"blank col22\" >&nbsp;</th>\n",
       "      <th class=\"blank col23\" >&nbsp;</th>\n",
       "      <th class=\"blank col24\" >&nbsp;</th>\n",
       "      <th class=\"blank col25\" >&nbsp;</th>\n",
       "      <th class=\"blank col26\" >&nbsp;</th>\n",
       "      <th class=\"blank col27\" >&nbsp;</th>\n",
       "      <th class=\"blank col28\" >&nbsp;</th>\n",
       "      <th class=\"blank col29\" >&nbsp;</th>\n",
       "      <th class=\"blank col30\" >&nbsp;</th>\n",
       "      <th class=\"blank col31\" >&nbsp;</th>\n",
       "      <th class=\"blank col32\" >&nbsp;</th>\n",
       "      <th class=\"blank col33\" >&nbsp;</th>\n",
       "      <th class=\"blank col34\" >&nbsp;</th>\n",
       "      <th class=\"blank col35\" >&nbsp;</th>\n",
       "      <th class=\"blank col36\" >&nbsp;</th>\n",
       "      <th class=\"blank col37\" >&nbsp;</th>\n",
       "      <th class=\"blank col38\" >&nbsp;</th>\n",
       "      <th class=\"blank col39\" >&nbsp;</th>\n",
       "      <th class=\"blank col40\" >&nbsp;</th>\n",
       "      <th class=\"blank col41\" >&nbsp;</th>\n",
       "      <th class=\"blank col42\" >&nbsp;</th>\n",
       "      <th class=\"blank col43\" >&nbsp;</th>\n",
       "      <th class=\"blank col44\" >&nbsp;</th>\n",
       "      <th class=\"blank col45\" >&nbsp;</th>\n",
       "      <th class=\"blank col46\" >&nbsp;</th>\n",
       "      <th class=\"blank col47\" >&nbsp;</th>\n",
       "      <th class=\"blank col48\" >&nbsp;</th>\n",
       "      <th class=\"blank col49\" >&nbsp;</th>\n",
       "      <th class=\"blank col50\" >&nbsp;</th>\n",
       "      <th class=\"blank col51\" >&nbsp;</th>\n",
       "      <th class=\"blank col52\" >&nbsp;</th>\n",
       "      <th class=\"blank col53\" >&nbsp;</th>\n",
       "      <th class=\"blank col54\" >&nbsp;</th>\n",
       "      <th class=\"blank col55\" >&nbsp;</th>\n",
       "      <th class=\"blank col56\" >&nbsp;</th>\n",
       "      <th class=\"blank col57\" >&nbsp;</th>\n",
       "      <th class=\"blank col58\" >&nbsp;</th>\n",
       "      <th class=\"blank col59\" >&nbsp;</th>\n",
       "      <th class=\"blank col60\" >&nbsp;</th>\n",
       "      <th class=\"blank col61\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row0\" class=\"row_heading level0 row0\" >- OK -</th>\n",
       "      <td id=\"T_aeb61_row0_col0\" class=\"data row0 col0\" >32062</td>\n",
       "      <td id=\"T_aeb61_row0_col1\" class=\"data row0 col1\" >676</td>\n",
       "      <td id=\"T_aeb61_row0_col2\" class=\"data row0 col2\" >208</td>\n",
       "      <td id=\"T_aeb61_row0_col3\" class=\"data row0 col3\" >900</td>\n",
       "      <td id=\"T_aeb61_row0_col4\" class=\"data row0 col4\" >549</td>\n",
       "      <td id=\"T_aeb61_row0_col5\" class=\"data row0 col5\" >903</td>\n",
       "      <td id=\"T_aeb61_row0_col6\" class=\"data row0 col6\" >203</td>\n",
       "      <td id=\"T_aeb61_row0_col7\" class=\"data row0 col7\" >686</td>\n",
       "      <td id=\"T_aeb61_row0_col8\" class=\"data row0 col8\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col9\" class=\"data row0 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row0_col10\" class=\"data row0 col10\" >287</td>\n",
       "      <td id=\"T_aeb61_row0_col11\" class=\"data row0 col11\" >66</td>\n",
       "      <td id=\"T_aeb61_row0_col12\" class=\"data row0 col12\" >923</td>\n",
       "      <td id=\"T_aeb61_row0_col13\" class=\"data row0 col13\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col14\" class=\"data row0 col14\" >711</td>\n",
       "      <td id=\"T_aeb61_row0_col15\" class=\"data row0 col15\" >903</td>\n",
       "      <td id=\"T_aeb61_row0_col16\" class=\"data row0 col16\" >555</td>\n",
       "      <td id=\"T_aeb61_row0_col17\" class=\"data row0 col17\" >23</td>\n",
       "      <td id=\"T_aeb61_row0_col18\" class=\"data row0 col18\" >5</td>\n",
       "      <td id=\"T_aeb61_row0_col19\" class=\"data row0 col19\" >319</td>\n",
       "      <td id=\"T_aeb61_row0_col20\" class=\"data row0 col20\" >870</td>\n",
       "      <td id=\"T_aeb61_row0_col21\" class=\"data row0 col21\" >298</td>\n",
       "      <td id=\"T_aeb61_row0_col22\" class=\"data row0 col22\" >903</td>\n",
       "      <td id=\"T_aeb61_row0_col23\" class=\"data row0 col23\" >903</td>\n",
       "      <td id=\"T_aeb61_row0_col24\" class=\"data row0 col24\" >902</td>\n",
       "      <td id=\"T_aeb61_row0_col25\" class=\"data row0 col25\" >87</td>\n",
       "      <td id=\"T_aeb61_row0_col26\" class=\"data row0 col26\" >288</td>\n",
       "      <td id=\"T_aeb61_row0_col27\" class=\"data row0 col27\" >710</td>\n",
       "      <td id=\"T_aeb61_row0_col28\" class=\"data row0 col28\" >737</td>\n",
       "      <td id=\"T_aeb61_row0_col29\" class=\"data row0 col29\" >1</td>\n",
       "      <td id=\"T_aeb61_row0_col30\" class=\"data row0 col30\" >621</td>\n",
       "      <td id=\"T_aeb61_row0_col31\" class=\"data row0 col31\" >1</td>\n",
       "      <td id=\"T_aeb61_row0_col32\" class=\"data row0 col32\" >733</td>\n",
       "      <td id=\"T_aeb61_row0_col33\" class=\"data row0 col33\" >258</td>\n",
       "      <td id=\"T_aeb61_row0_col34\" class=\"data row0 col34\" >352</td>\n",
       "      <td id=\"T_aeb61_row0_col35\" class=\"data row0 col35\" >903</td>\n",
       "      <td id=\"T_aeb61_row0_col36\" class=\"data row0 col36\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col37\" class=\"data row0 col37\" >705</td>\n",
       "      <td id=\"T_aeb61_row0_col38\" class=\"data row0 col38\" >626</td>\n",
       "      <td id=\"T_aeb61_row0_col39\" class=\"data row0 col39\" >284</td>\n",
       "      <td id=\"T_aeb61_row0_col40\" class=\"data row0 col40\" >250</td>\n",
       "      <td id=\"T_aeb61_row0_col41\" class=\"data row0 col41\" >331</td>\n",
       "      <td id=\"T_aeb61_row0_col42\" class=\"data row0 col42\" >294</td>\n",
       "      <td id=\"T_aeb61_row0_col43\" class=\"data row0 col43\" >508</td>\n",
       "      <td id=\"T_aeb61_row0_col44\" class=\"data row0 col44\" >194</td>\n",
       "      <td id=\"T_aeb61_row0_col45\" class=\"data row0 col45\" >733</td>\n",
       "      <td id=\"T_aeb61_row0_col46\" class=\"data row0 col46\" >699</td>\n",
       "      <td id=\"T_aeb61_row0_col47\" class=\"data row0 col47\" >723</td>\n",
       "      <td id=\"T_aeb61_row0_col48\" class=\"data row0 col48\" >693</td>\n",
       "      <td id=\"T_aeb61_row0_col49\" class=\"data row0 col49\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col50\" class=\"data row0 col50\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col51\" class=\"data row0 col51\" >720</td>\n",
       "      <td id=\"T_aeb61_row0_col52\" class=\"data row0 col52\" >253</td>\n",
       "      <td id=\"T_aeb61_row0_col53\" class=\"data row0 col53\" >106</td>\n",
       "      <td id=\"T_aeb61_row0_col54\" class=\"data row0 col54\" >738</td>\n",
       "      <td id=\"T_aeb61_row0_col55\" class=\"data row0 col55\" >327</td>\n",
       "      <td id=\"T_aeb61_row0_col56\" class=\"data row0 col56\" >737</td>\n",
       "      <td id=\"T_aeb61_row0_col57\" class=\"data row0 col57\" >525</td>\n",
       "      <td id=\"T_aeb61_row0_col58\" class=\"data row0 col58\" >558</td>\n",
       "      <td id=\"T_aeb61_row0_col59\" class=\"data row0 col59\" >301</td>\n",
       "      <td id=\"T_aeb61_row0_col60\" class=\"data row0 col60\" >753</td>\n",
       "      <td id=\"T_aeb61_row0_col61\" class=\"data row0 col61\" >830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row1\" class=\"row_heading level0 row1\" >- OOM -</th>\n",
       "      <td id=\"T_aeb61_row1_col0\" class=\"data row1 col0\" >1257</td>\n",
       "      <td id=\"T_aeb61_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col4\" class=\"data row1 col4\" >354</td>\n",
       "      <td id=\"T_aeb61_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col6\" class=\"data row1 col6\" >77</td>\n",
       "      <td id=\"T_aeb61_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col9\" class=\"data row1 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col10\" class=\"data row1 col10\" >3</td>\n",
       "      <td id=\"T_aeb61_row1_col11\" class=\"data row1 col11\" >96</td>\n",
       "      <td id=\"T_aeb61_row1_col12\" class=\"data row1 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col13\" class=\"data row1 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col14\" class=\"data row1 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col15\" class=\"data row1 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col16\" class=\"data row1 col16\" >10</td>\n",
       "      <td id=\"T_aeb61_row1_col17\" class=\"data row1 col17\" >7</td>\n",
       "      <td id=\"T_aeb61_row1_col18\" class=\"data row1 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col19\" class=\"data row1 col19\" >6</td>\n",
       "      <td id=\"T_aeb61_row1_col20\" class=\"data row1 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col21\" class=\"data row1 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col22\" class=\"data row1 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col23\" class=\"data row1 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col24\" class=\"data row1 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col25\" class=\"data row1 col25\" >210</td>\n",
       "      <td id=\"T_aeb61_row1_col26\" class=\"data row1 col26\" >1</td>\n",
       "      <td id=\"T_aeb61_row1_col27\" class=\"data row1 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col28\" class=\"data row1 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col29\" class=\"data row1 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col30\" class=\"data row1 col30\" >13</td>\n",
       "      <td id=\"T_aeb61_row1_col31\" class=\"data row1 col31\" >2</td>\n",
       "      <td id=\"T_aeb61_row1_col32\" class=\"data row1 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col33\" class=\"data row1 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col34\" class=\"data row1 col34\" >20</td>\n",
       "      <td id=\"T_aeb61_row1_col35\" class=\"data row1 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col36\" class=\"data row1 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col37\" class=\"data row1 col37\" >32</td>\n",
       "      <td id=\"T_aeb61_row1_col38\" class=\"data row1 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col39\" class=\"data row1 col39\" >75</td>\n",
       "      <td id=\"T_aeb61_row1_col40\" class=\"data row1 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col41\" class=\"data row1 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col42\" class=\"data row1 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col43\" class=\"data row1 col43\" >12</td>\n",
       "      <td id=\"T_aeb61_row1_col44\" class=\"data row1 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col45\" class=\"data row1 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col46\" class=\"data row1 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col47\" class=\"data row1 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col48\" class=\"data row1 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col49\" class=\"data row1 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col50\" class=\"data row1 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col51\" class=\"data row1 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col52\" class=\"data row1 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col53\" class=\"data row1 col53\" >48</td>\n",
       "      <td id=\"T_aeb61_row1_col54\" class=\"data row1 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col55\" class=\"data row1 col55\" >50</td>\n",
       "      <td id=\"T_aeb61_row1_col56\" class=\"data row1 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col57\" class=\"data row1 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row1_col58\" class=\"data row1 col58\" >77</td>\n",
       "      <td id=\"T_aeb61_row1_col59\" class=\"data row1 col59\" >3</td>\n",
       "      <td id=\"T_aeb61_row1_col60\" class=\"data row1 col60\" >161</td>\n",
       "      <td id=\"T_aeb61_row1_col61\" class=\"data row1 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row2\" class=\"row_heading level0 row2\" >- TIMEOUT -</th>\n",
       "      <td id=\"T_aeb61_row2_col0\" class=\"data row2 col0\" >2228</td>\n",
       "      <td id=\"T_aeb61_row2_col1\" class=\"data row2 col1\" >58</td>\n",
       "      <td id=\"T_aeb61_row2_col2\" class=\"data row2 col2\" >87</td>\n",
       "      <td id=\"T_aeb61_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "      <td id=\"T_aeb61_row2_col7\" class=\"data row2 col7\" >52</td>\n",
       "      <td id=\"T_aeb61_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col9\" class=\"data row2 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col10\" class=\"data row2 col10\" >4</td>\n",
       "      <td id=\"T_aeb61_row2_col11\" class=\"data row2 col11\" >200</td>\n",
       "      <td id=\"T_aeb61_row2_col12\" class=\"data row2 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col13\" class=\"data row2 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col14\" class=\"data row2 col14\" >27</td>\n",
       "      <td id=\"T_aeb61_row2_col15\" class=\"data row2 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col16\" class=\"data row2 col16\" >164</td>\n",
       "      <td id=\"T_aeb61_row2_col17\" class=\"data row2 col17\" >349</td>\n",
       "      <td id=\"T_aeb61_row2_col18\" class=\"data row2 col18\" >1</td>\n",
       "      <td id=\"T_aeb61_row2_col19\" class=\"data row2 col19\" >48</td>\n",
       "      <td id=\"T_aeb61_row2_col20\" class=\"data row2 col20\" >6</td>\n",
       "      <td id=\"T_aeb61_row2_col21\" class=\"data row2 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col22\" class=\"data row2 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col23\" class=\"data row2 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col24\" class=\"data row2 col24\" >1</td>\n",
       "      <td id=\"T_aeb61_row2_col25\" class=\"data row2 col25\" >66</td>\n",
       "      <td id=\"T_aeb61_row2_col26\" class=\"data row2 col26\" >74</td>\n",
       "      <td id=\"T_aeb61_row2_col27\" class=\"data row2 col27\" >16</td>\n",
       "      <td id=\"T_aeb61_row2_col28\" class=\"data row2 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col29\" class=\"data row2 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col30\" class=\"data row2 col30\" >78</td>\n",
       "      <td id=\"T_aeb61_row2_col31\" class=\"data row2 col31\" >3</td>\n",
       "      <td id=\"T_aeb61_row2_col32\" class=\"data row2 col32\" >4</td>\n",
       "      <td id=\"T_aeb61_row2_col33\" class=\"data row2 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col34\" class=\"data row2 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col35\" class=\"data row2 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col36\" class=\"data row2 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col37\" class=\"data row2 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col38\" class=\"data row2 col38\" >109</td>\n",
       "      <td id=\"T_aeb61_row2_col39\" class=\"data row2 col39\" >20</td>\n",
       "      <td id=\"T_aeb61_row2_col40\" class=\"data row2 col40\" >54</td>\n",
       "      <td id=\"T_aeb61_row2_col41\" class=\"data row2 col41\" >48</td>\n",
       "      <td id=\"T_aeb61_row2_col42\" class=\"data row2 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col43\" class=\"data row2 col43\" >46</td>\n",
       "      <td id=\"T_aeb61_row2_col44\" class=\"data row2 col44\" >103</td>\n",
       "      <td id=\"T_aeb61_row2_col45\" class=\"data row2 col45\" >5</td>\n",
       "      <td id=\"T_aeb61_row2_col46\" class=\"data row2 col46\" >39</td>\n",
       "      <td id=\"T_aeb61_row2_col47\" class=\"data row2 col47\" >15</td>\n",
       "      <td id=\"T_aeb61_row2_col48\" class=\"data row2 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col49\" class=\"data row2 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col50\" class=\"data row2 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col51\" class=\"data row2 col51\" >18</td>\n",
       "      <td id=\"T_aeb61_row2_col52\" class=\"data row2 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col53\" class=\"data row2 col53\" >221</td>\n",
       "      <td id=\"T_aeb61_row2_col54\" class=\"data row2 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col55\" class=\"data row2 col55\" >1</td>\n",
       "      <td id=\"T_aeb61_row2_col56\" class=\"data row2 col56\" >163</td>\n",
       "      <td id=\"T_aeb61_row2_col57\" class=\"data row2 col57\" >135</td>\n",
       "      <td id=\"T_aeb61_row2_col58\" class=\"data row2 col58\" >12</td>\n",
       "      <td id=\"T_aeb61_row2_col59\" class=\"data row2 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col60\" class=\"data row2 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row2_col61\" class=\"data row2 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row3\" class=\"row_heading level0 row3\" >Bug</th>\n",
       "      <td id=\"T_aeb61_row3_col0\" class=\"data row3 col0\" >874</td>\n",
       "      <td id=\"T_aeb61_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col2\" class=\"data row3 col2\" >9</td>\n",
       "      <td id=\"T_aeb61_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col6\" class=\"data row3 col6\" >404</td>\n",
       "      <td id=\"T_aeb61_row3_col7\" class=\"data row3 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col9\" class=\"data row3 col9\" >12</td>\n",
       "      <td id=\"T_aeb61_row3_col10\" class=\"data row3 col10\" >10</td>\n",
       "      <td id=\"T_aeb61_row3_col11\" class=\"data row3 col11\" >17</td>\n",
       "      <td id=\"T_aeb61_row3_col12\" class=\"data row3 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col13\" class=\"data row3 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col14\" class=\"data row3 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col15\" class=\"data row3 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col16\" class=\"data row3 col16\" >9</td>\n",
       "      <td id=\"T_aeb61_row3_col17\" class=\"data row3 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col18\" class=\"data row3 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col19\" class=\"data row3 col19\" >6</td>\n",
       "      <td id=\"T_aeb61_row3_col20\" class=\"data row3 col20\" >1</td>\n",
       "      <td id=\"T_aeb61_row3_col21\" class=\"data row3 col21\" >6</td>\n",
       "      <td id=\"T_aeb61_row3_col22\" class=\"data row3 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col23\" class=\"data row3 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col24\" class=\"data row3 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col25\" class=\"data row3 col25\" >16</td>\n",
       "      <td id=\"T_aeb61_row3_col26\" class=\"data row3 col26\" >16</td>\n",
       "      <td id=\"T_aeb61_row3_col27\" class=\"data row3 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col28\" class=\"data row3 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col29\" class=\"data row3 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col30\" class=\"data row3 col30\" >9</td>\n",
       "      <td id=\"T_aeb61_row3_col31\" class=\"data row3 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col32\" class=\"data row3 col32\" >1</td>\n",
       "      <td id=\"T_aeb61_row3_col33\" class=\"data row3 col33\" >43</td>\n",
       "      <td id=\"T_aeb61_row3_col34\" class=\"data row3 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col35\" class=\"data row3 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col36\" class=\"data row3 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col37\" class=\"data row3 col37\" >1</td>\n",
       "      <td id=\"T_aeb61_row3_col38\" class=\"data row3 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col39\" class=\"data row3 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col40\" class=\"data row3 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col41\" class=\"data row3 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col42\" class=\"data row3 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col43\" class=\"data row3 col43\" >162</td>\n",
       "      <td id=\"T_aeb61_row3_col44\" class=\"data row3 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col45\" class=\"data row3 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col46\" class=\"data row3 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col47\" class=\"data row3 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col48\" class=\"data row3 col48\" >31</td>\n",
       "      <td id=\"T_aeb61_row3_col49\" class=\"data row3 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col50\" class=\"data row3 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col51\" class=\"data row3 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col52\" class=\"data row3 col52\" >20</td>\n",
       "      <td id=\"T_aeb61_row3_col53\" class=\"data row3 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col54\" class=\"data row3 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col55\" class=\"data row3 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col56\" class=\"data row3 col56\" >2</td>\n",
       "      <td id=\"T_aeb61_row3_col57\" class=\"data row3 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col58\" class=\"data row3 col58\" >90</td>\n",
       "      <td id=\"T_aeb61_row3_col59\" class=\"data row3 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row3_col60\" class=\"data row3 col60\" >9</td>\n",
       "      <td id=\"T_aeb61_row3_col61\" class=\"data row3 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row4\" class=\"row_heading level0 row4\" >Incompatible parameters</th>\n",
       "      <td id=\"T_aeb61_row4_col0\" class=\"data row4 col0\" >663</td>\n",
       "      <td id=\"T_aeb61_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col6\" class=\"data row4 col6\" >227</td>\n",
       "      <td id=\"T_aeb61_row4_col7\" class=\"data row4 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col8\" class=\"data row4 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col9\" class=\"data row4 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col10\" class=\"data row4 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col11\" class=\"data row4 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col12\" class=\"data row4 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col13\" class=\"data row4 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col14\" class=\"data row4 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col15\" class=\"data row4 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col16\" class=\"data row4 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col17\" class=\"data row4 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col18\" class=\"data row4 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col19\" class=\"data row4 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col20\" class=\"data row4 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col21\" class=\"data row4 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col22\" class=\"data row4 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col23\" class=\"data row4 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col24\" class=\"data row4 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col25\" class=\"data row4 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col26\" class=\"data row4 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col27\" class=\"data row4 col27\" >2</td>\n",
       "      <td id=\"T_aeb61_row4_col28\" class=\"data row4 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col29\" class=\"data row4 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col30\" class=\"data row4 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col31\" class=\"data row4 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col32\" class=\"data row4 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col33\" class=\"data row4 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col34\" class=\"data row4 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col35\" class=\"data row4 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col36\" class=\"data row4 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col37\" class=\"data row4 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col38\" class=\"data row4 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col39\" class=\"data row4 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col40\" class=\"data row4 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col41\" class=\"data row4 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col42\" class=\"data row4 col42\" >434</td>\n",
       "      <td id=\"T_aeb61_row4_col43\" class=\"data row4 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col44\" class=\"data row4 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col45\" class=\"data row4 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col46\" class=\"data row4 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col47\" class=\"data row4 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col48\" class=\"data row4 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col49\" class=\"data row4 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col50\" class=\"data row4 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col51\" class=\"data row4 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col52\" class=\"data row4 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col53\" class=\"data row4 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col54\" class=\"data row4 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col55\" class=\"data row4 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col56\" class=\"data row4 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col57\" class=\"data row4 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col58\" class=\"data row4 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col59\" class=\"data row4 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col60\" class=\"data row4 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row4_col61\" class=\"data row4 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row5\" class=\"row_heading level0 row5\" >Invariance/assumption not met</th>\n",
       "      <td id=\"T_aeb61_row5_col0\" class=\"data row5 col0\" >121</td>\n",
       "      <td id=\"T_aeb61_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col6\" class=\"data row5 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col7\" class=\"data row5 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col8\" class=\"data row5 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col9\" class=\"data row5 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col10\" class=\"data row5 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col11\" class=\"data row5 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col12\" class=\"data row5 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col13\" class=\"data row5 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col14\" class=\"data row5 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col15\" class=\"data row5 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col16\" class=\"data row5 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col17\" class=\"data row5 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col18\" class=\"data row5 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col19\" class=\"data row5 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col20\" class=\"data row5 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col21\" class=\"data row5 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col22\" class=\"data row5 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col23\" class=\"data row5 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col24\" class=\"data row5 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col25\" class=\"data row5 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col26\" class=\"data row5 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col27\" class=\"data row5 col27\" >10</td>\n",
       "      <td id=\"T_aeb61_row5_col28\" class=\"data row5 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col29\" class=\"data row5 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col30\" class=\"data row5 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col31\" class=\"data row5 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col32\" class=\"data row5 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col33\" class=\"data row5 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col34\" class=\"data row5 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col35\" class=\"data row5 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col36\" class=\"data row5 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col37\" class=\"data row5 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col38\" class=\"data row5 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col39\" class=\"data row5 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col40\" class=\"data row5 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col41\" class=\"data row5 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col42\" class=\"data row5 col42\" >10</td>\n",
       "      <td id=\"T_aeb61_row5_col43\" class=\"data row5 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col44\" class=\"data row5 col44\" >7</td>\n",
       "      <td id=\"T_aeb61_row5_col45\" class=\"data row5 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col46\" class=\"data row5 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col47\" class=\"data row5 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col48\" class=\"data row5 col48\" >12</td>\n",
       "      <td id=\"T_aeb61_row5_col49\" class=\"data row5 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col50\" class=\"data row5 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col51\" class=\"data row5 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col52\" class=\"data row5 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col53\" class=\"data row5 col53\" >4</td>\n",
       "      <td id=\"T_aeb61_row5_col54\" class=\"data row5 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col55\" class=\"data row5 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col56\" class=\"data row5 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col57\" class=\"data row5 col57\" >78</td>\n",
       "      <td id=\"T_aeb61_row5_col58\" class=\"data row5 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col59\" class=\"data row5 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col60\" class=\"data row5 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row5_col61\" class=\"data row5 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row6\" class=\"row_heading level0 row6\" >LinAlgError</th>\n",
       "      <td id=\"T_aeb61_row6_col0\" class=\"data row6 col0\" >21</td>\n",
       "      <td id=\"T_aeb61_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_aeb61_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col6\" class=\"data row6 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col7\" class=\"data row6 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col8\" class=\"data row6 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col9\" class=\"data row6 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col10\" class=\"data row6 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col11\" class=\"data row6 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col12\" class=\"data row6 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col13\" class=\"data row6 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col14\" class=\"data row6 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col15\" class=\"data row6 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col16\" class=\"data row6 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col17\" class=\"data row6 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col18\" class=\"data row6 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col19\" class=\"data row6 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col20\" class=\"data row6 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col21\" class=\"data row6 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col22\" class=\"data row6 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col23\" class=\"data row6 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col24\" class=\"data row6 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col25\" class=\"data row6 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col26\" class=\"data row6 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col27\" class=\"data row6 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col28\" class=\"data row6 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col29\" class=\"data row6 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col30\" class=\"data row6 col30\" >17</td>\n",
       "      <td id=\"T_aeb61_row6_col31\" class=\"data row6 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col32\" class=\"data row6 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col33\" class=\"data row6 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col34\" class=\"data row6 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col35\" class=\"data row6 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col36\" class=\"data row6 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col37\" class=\"data row6 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col38\" class=\"data row6 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col39\" class=\"data row6 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col40\" class=\"data row6 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col41\" class=\"data row6 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col42\" class=\"data row6 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col43\" class=\"data row6 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col44\" class=\"data row6 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col45\" class=\"data row6 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col46\" class=\"data row6 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col47\" class=\"data row6 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col48\" class=\"data row6 col48\" >2</td>\n",
       "      <td id=\"T_aeb61_row6_col49\" class=\"data row6 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col50\" class=\"data row6 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col51\" class=\"data row6 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col52\" class=\"data row6 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col53\" class=\"data row6 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col54\" class=\"data row6 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col55\" class=\"data row6 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col56\" class=\"data row6 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col57\" class=\"data row6 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col58\" class=\"data row6 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col59\" class=\"data row6 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col60\" class=\"data row6 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row6_col61\" class=\"data row6 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row7\" class=\"row_heading level0 row7\" >Max recursion depth exceeded</th>\n",
       "      <td id=\"T_aeb61_row7_col0\" class=\"data row7 col0\" >30</td>\n",
       "      <td id=\"T_aeb61_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col4\" class=\"data row7 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col5\" class=\"data row7 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col6\" class=\"data row7 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col7\" class=\"data row7 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col8\" class=\"data row7 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col9\" class=\"data row7 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col10\" class=\"data row7 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col11\" class=\"data row7 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col12\" class=\"data row7 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col13\" class=\"data row7 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col14\" class=\"data row7 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col15\" class=\"data row7 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col16\" class=\"data row7 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col17\" class=\"data row7 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col18\" class=\"data row7 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col19\" class=\"data row7 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col20\" class=\"data row7 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col21\" class=\"data row7 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col22\" class=\"data row7 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col23\" class=\"data row7 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col24\" class=\"data row7 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col25\" class=\"data row7 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col26\" class=\"data row7 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col27\" class=\"data row7 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col28\" class=\"data row7 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col29\" class=\"data row7 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col30\" class=\"data row7 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col31\" class=\"data row7 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col32\" class=\"data row7 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col33\" class=\"data row7 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col34\" class=\"data row7 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col35\" class=\"data row7 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col36\" class=\"data row7 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col37\" class=\"data row7 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col38\" class=\"data row7 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col39\" class=\"data row7 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col40\" class=\"data row7 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col41\" class=\"data row7 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col42\" class=\"data row7 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col43\" class=\"data row7 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col44\" class=\"data row7 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col45\" class=\"data row7 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col46\" class=\"data row7 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col47\" class=\"data row7 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col48\" class=\"data row7 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col49\" class=\"data row7 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col50\" class=\"data row7 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col51\" class=\"data row7 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col52\" class=\"data row7 col52\" >30</td>\n",
       "      <td id=\"T_aeb61_row7_col53\" class=\"data row7 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col54\" class=\"data row7 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col55\" class=\"data row7 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col56\" class=\"data row7 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col57\" class=\"data row7 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col58\" class=\"data row7 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col59\" class=\"data row7 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col60\" class=\"data row7 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row7_col61\" class=\"data row7 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row8\" class=\"row_heading level0 row8\" >Model loading error</th>\n",
       "      <td id=\"T_aeb61_row8_col0\" class=\"data row8 col0\" >5</td>\n",
       "      <td id=\"T_aeb61_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col4\" class=\"data row8 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col5\" class=\"data row8 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col6\" class=\"data row8 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col7\" class=\"data row8 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col8\" class=\"data row8 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col9\" class=\"data row8 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col10\" class=\"data row8 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col11\" class=\"data row8 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col12\" class=\"data row8 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col13\" class=\"data row8 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col14\" class=\"data row8 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col15\" class=\"data row8 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col16\" class=\"data row8 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col17\" class=\"data row8 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col18\" class=\"data row8 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col19\" class=\"data row8 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col20\" class=\"data row8 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col21\" class=\"data row8 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col22\" class=\"data row8 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col23\" class=\"data row8 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col24\" class=\"data row8 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col25\" class=\"data row8 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col26\" class=\"data row8 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col27\" class=\"data row8 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col28\" class=\"data row8 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col29\" class=\"data row8 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col30\" class=\"data row8 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col31\" class=\"data row8 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col32\" class=\"data row8 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col33\" class=\"data row8 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col34\" class=\"data row8 col34\" >5</td>\n",
       "      <td id=\"T_aeb61_row8_col35\" class=\"data row8 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col36\" class=\"data row8 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col37\" class=\"data row8 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col38\" class=\"data row8 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col39\" class=\"data row8 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col40\" class=\"data row8 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col41\" class=\"data row8 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col42\" class=\"data row8 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col43\" class=\"data row8 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col44\" class=\"data row8 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col45\" class=\"data row8 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col46\" class=\"data row8 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col47\" class=\"data row8 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col48\" class=\"data row8 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col49\" class=\"data row8 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col50\" class=\"data row8 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col51\" class=\"data row8 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col52\" class=\"data row8 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col53\" class=\"data row8 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col54\" class=\"data row8 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col55\" class=\"data row8 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col56\" class=\"data row8 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col57\" class=\"data row8 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col58\" class=\"data row8 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col59\" class=\"data row8 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col60\" class=\"data row8 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row8_col61\" class=\"data row8 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row9\" class=\"row_heading level0 row9\" >Not converged</th>\n",
       "      <td id=\"T_aeb61_row9_col0\" class=\"data row9 col0\" >8</td>\n",
       "      <td id=\"T_aeb61_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col3\" class=\"data row9 col3\" >3</td>\n",
       "      <td id=\"T_aeb61_row9_col4\" class=\"data row9 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col5\" class=\"data row9 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col6\" class=\"data row9 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col7\" class=\"data row9 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col8\" class=\"data row9 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col9\" class=\"data row9 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col10\" class=\"data row9 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col11\" class=\"data row9 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col12\" class=\"data row9 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col13\" class=\"data row9 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col14\" class=\"data row9 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col15\" class=\"data row9 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col16\" class=\"data row9 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col17\" class=\"data row9 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col18\" class=\"data row9 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col19\" class=\"data row9 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col20\" class=\"data row9 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col21\" class=\"data row9 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col22\" class=\"data row9 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col23\" class=\"data row9 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col24\" class=\"data row9 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col25\" class=\"data row9 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col26\" class=\"data row9 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col27\" class=\"data row9 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col28\" class=\"data row9 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col29\" class=\"data row9 col29\" >5</td>\n",
       "      <td id=\"T_aeb61_row9_col30\" class=\"data row9 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col31\" class=\"data row9 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col32\" class=\"data row9 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col33\" class=\"data row9 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col34\" class=\"data row9 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col35\" class=\"data row9 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col36\" class=\"data row9 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col37\" class=\"data row9 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col38\" class=\"data row9 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col39\" class=\"data row9 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col40\" class=\"data row9 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col41\" class=\"data row9 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col42\" class=\"data row9 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col43\" class=\"data row9 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col44\" class=\"data row9 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col45\" class=\"data row9 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col46\" class=\"data row9 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col47\" class=\"data row9 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col48\" class=\"data row9 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col49\" class=\"data row9 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col50\" class=\"data row9 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col51\" class=\"data row9 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col52\" class=\"data row9 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col53\" class=\"data row9 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col54\" class=\"data row9 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col55\" class=\"data row9 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col56\" class=\"data row9 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col57\" class=\"data row9 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col58\" class=\"data row9 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col59\" class=\"data row9 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col60\" class=\"data row9 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row9_col61\" class=\"data row9 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row10\" class=\"row_heading level0 row10\" >TimeEval:KilledWorker</th>\n",
       "      <td id=\"T_aeb61_row10_col0\" class=\"data row10 col0\" >97</td>\n",
       "      <td id=\"T_aeb61_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col3\" class=\"data row10 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col4\" class=\"data row10 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col5\" class=\"data row10 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col6\" class=\"data row10 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col7\" class=\"data row10 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col8\" class=\"data row10 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col9\" class=\"data row10 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col10\" class=\"data row10 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col11\" class=\"data row10 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col12\" class=\"data row10 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col14\" class=\"data row10 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col15\" class=\"data row10 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col16\" class=\"data row10 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col17\" class=\"data row10 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col18\" class=\"data row10 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col19\" class=\"data row10 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col20\" class=\"data row10 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col21\" class=\"data row10 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col22\" class=\"data row10 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col23\" class=\"data row10 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col24\" class=\"data row10 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col25\" class=\"data row10 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col26\" class=\"data row10 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col27\" class=\"data row10 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col28\" class=\"data row10 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col29\" class=\"data row10 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col30\" class=\"data row10 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col31\" class=\"data row10 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col32\" class=\"data row10 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col33\" class=\"data row10 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col34\" class=\"data row10 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col35\" class=\"data row10 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col36\" class=\"data row10 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col37\" class=\"data row10 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col38\" class=\"data row10 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col39\" class=\"data row10 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col40\" class=\"data row10 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col41\" class=\"data row10 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col42\" class=\"data row10 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col43\" class=\"data row10 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col44\" class=\"data row10 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col45\" class=\"data row10 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col46\" class=\"data row10 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col47\" class=\"data row10 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col48\" class=\"data row10 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col49\" class=\"data row10 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col50\" class=\"data row10 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col51\" class=\"data row10 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col52\" class=\"data row10 col52\" >1</td>\n",
       "      <td id=\"T_aeb61_row10_col53\" class=\"data row10 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col54\" class=\"data row10 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col55\" class=\"data row10 col55\" >1</td>\n",
       "      <td id=\"T_aeb61_row10_col56\" class=\"data row10 col56\" >1</td>\n",
       "      <td id=\"T_aeb61_row10_col57\" class=\"data row10 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col58\" class=\"data row10 col58\" >1</td>\n",
       "      <td id=\"T_aeb61_row10_col59\" class=\"data row10 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col60\" class=\"data row10 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row10_col61\" class=\"data row10 col61\" >93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row11\" class=\"row_heading level0 row11\" >Wrong shape error</th>\n",
       "      <td id=\"T_aeb61_row11_col0\" class=\"data row11 col0\" >14</td>\n",
       "      <td id=\"T_aeb61_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col3\" class=\"data row11 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col4\" class=\"data row11 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col5\" class=\"data row11 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col6\" class=\"data row11 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col7\" class=\"data row11 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col8\" class=\"data row11 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col9\" class=\"data row11 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col10\" class=\"data row11 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col11\" class=\"data row11 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col14\" class=\"data row11 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col15\" class=\"data row11 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col16\" class=\"data row11 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col17\" class=\"data row11 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col18\" class=\"data row11 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col19\" class=\"data row11 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col20\" class=\"data row11 col20\" >1</td>\n",
       "      <td id=\"T_aeb61_row11_col21\" class=\"data row11 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col22\" class=\"data row11 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col23\" class=\"data row11 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col24\" class=\"data row11 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col25\" class=\"data row11 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col26\" class=\"data row11 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col27\" class=\"data row11 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col28\" class=\"data row11 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col29\" class=\"data row11 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col30\" class=\"data row11 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col31\" class=\"data row11 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col32\" class=\"data row11 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col33\" class=\"data row11 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col34\" class=\"data row11 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col35\" class=\"data row11 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col36\" class=\"data row11 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col37\" class=\"data row11 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col38\" class=\"data row11 col38\" >3</td>\n",
       "      <td id=\"T_aeb61_row11_col39\" class=\"data row11 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col40\" class=\"data row11 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col41\" class=\"data row11 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col42\" class=\"data row11 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col43\" class=\"data row11 col43\" >10</td>\n",
       "      <td id=\"T_aeb61_row11_col44\" class=\"data row11 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col45\" class=\"data row11 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col46\" class=\"data row11 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col47\" class=\"data row11 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col48\" class=\"data row11 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col49\" class=\"data row11 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col50\" class=\"data row11 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col51\" class=\"data row11 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col52\" class=\"data row11 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col53\" class=\"data row11 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col54\" class=\"data row11 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col55\" class=\"data row11 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col56\" class=\"data row11 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col57\" class=\"data row11 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col58\" class=\"data row11 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col59\" class=\"data row11 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col60\" class=\"data row11 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row11_col61\" class=\"data row11 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row12\" class=\"row_heading level0 row12\" >other</th>\n",
       "      <td id=\"T_aeb61_row12_col0\" class=\"data row12 col0\" >385</td>\n",
       "      <td id=\"T_aeb61_row12_col1\" class=\"data row12 col1\" >1</td>\n",
       "      <td id=\"T_aeb61_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col3\" class=\"data row12 col3\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col4\" class=\"data row12 col4\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col5\" class=\"data row12 col5\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col6\" class=\"data row12 col6\" >11</td>\n",
       "      <td id=\"T_aeb61_row12_col7\" class=\"data row12 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col8\" class=\"data row12 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col9\" class=\"data row12 col9\" >367</td>\n",
       "      <td id=\"T_aeb61_row12_col10\" class=\"data row12 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col14\" class=\"data row12 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col15\" class=\"data row12 col15\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col16\" class=\"data row12 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col17\" class=\"data row12 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col18\" class=\"data row12 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col19\" class=\"data row12 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col20\" class=\"data row12 col20\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col21\" class=\"data row12 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col22\" class=\"data row12 col22\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col23\" class=\"data row12 col23\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col24\" class=\"data row12 col24\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col25\" class=\"data row12 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col26\" class=\"data row12 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col27\" class=\"data row12 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col28\" class=\"data row12 col28\" >1</td>\n",
       "      <td id=\"T_aeb61_row12_col29\" class=\"data row12 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col30\" class=\"data row12 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col31\" class=\"data row12 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col32\" class=\"data row12 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col33\" class=\"data row12 col33\" >3</td>\n",
       "      <td id=\"T_aeb61_row12_col34\" class=\"data row12 col34\" >2</td>\n",
       "      <td id=\"T_aeb61_row12_col35\" class=\"data row12 col35\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col36\" class=\"data row12 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col37\" class=\"data row12 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col38\" class=\"data row12 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col39\" class=\"data row12 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col40\" class=\"data row12 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col41\" class=\"data row12 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col42\" class=\"data row12 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col43\" class=\"data row12 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col44\" class=\"data row12 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col45\" class=\"data row12 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col46\" class=\"data row12 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col47\" class=\"data row12 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col48\" class=\"data row12 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col49\" class=\"data row12 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col50\" class=\"data row12 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col51\" class=\"data row12 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col52\" class=\"data row12 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col53\" class=\"data row12 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col54\" class=\"data row12 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col55\" class=\"data row12 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col56\" class=\"data row12 col56\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col57\" class=\"data row12 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col58\" class=\"data row12 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col59\" class=\"data row12 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col60\" class=\"data row12 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row12_col61\" class=\"data row12 col61\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aeb61_level0_row13\" class=\"row_heading level0 row13\" >unexpected Inf or NaN</th>\n",
       "      <td id=\"T_aeb61_row13_col0\" class=\"data row13 col0\" >226</td>\n",
       "      <td id=\"T_aeb61_row13_col1\" class=\"data row13 col1\" >1</td>\n",
       "      <td id=\"T_aeb61_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col3\" class=\"data row13 col3\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col4\" class=\"data row13 col4\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col5\" class=\"data row13 col5\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col7\" class=\"data row13 col7\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col8\" class=\"data row13 col8\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col14\" class=\"data row13 col14\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col15\" class=\"data row13 col15\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col16\" class=\"data row13 col16\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col17\" class=\"data row13 col17\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col18\" class=\"data row13 col18\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col19\" class=\"data row13 col19\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col20\" class=\"data row13 col20\" >45</td>\n",
       "      <td id=\"T_aeb61_row13_col21\" class=\"data row13 col21\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col22\" class=\"data row13 col22\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col23\" class=\"data row13 col23\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col24\" class=\"data row13 col24\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col25\" class=\"data row13 col25\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col26\" class=\"data row13 col26\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col27\" class=\"data row13 col27\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col28\" class=\"data row13 col28\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col29\" class=\"data row13 col29\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col30\" class=\"data row13 col30\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col31\" class=\"data row13 col31\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col32\" class=\"data row13 col32\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col33\" class=\"data row13 col33\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col34\" class=\"data row13 col34\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col35\" class=\"data row13 col35\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col36\" class=\"data row13 col36\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col37\" class=\"data row13 col37\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col38\" class=\"data row13 col38\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col39\" class=\"data row13 col39\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col40\" class=\"data row13 col40\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col41\" class=\"data row13 col41\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col42\" class=\"data row13 col42\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col43\" class=\"data row13 col43\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col44\" class=\"data row13 col44\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col45\" class=\"data row13 col45\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col46\" class=\"data row13 col46\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col47\" class=\"data row13 col47\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col48\" class=\"data row13 col48\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col49\" class=\"data row13 col49\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col50\" class=\"data row13 col50\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col51\" class=\"data row13 col51\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col52\" class=\"data row13 col52\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col53\" class=\"data row13 col53\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col54\" class=\"data row13 col54\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col55\" class=\"data row13 col55\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col56\" class=\"data row13 col56\" >20</td>\n",
       "      <td id=\"T_aeb61_row13_col57\" class=\"data row13 col57\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col58\" class=\"data row13 col58\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col59\" class=\"data row13 col59\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col60\" class=\"data row13 col60\" ></td>\n",
       "      <td id=\"T_aeb61_row13_col61\" class=\"data row13 col61\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1d1920e0a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ok = \"- OK -\"\n",
    "oom = \"- OOM -\"\n",
    "timeout = \"- TIMEOUT -\"\n",
    "error_mapping = {\n",
    "    \"TimeoutError\": timeout,\n",
    "    \"status code '137'\": oom,\n",
    "    \"MemoryError: Unable to allocate\": oom,\n",
    "    \"ValueError: Expected 2D array, got 1D array instead\": \"Wrong shape error\",\n",
    "    \"could not broadcast input array from shape\": \"Wrong shape error\",\n",
    "    \"not aligned\": \"Wrong shape error\",  # shapes (20,) and (19,500) not aligned\n",
    "    \"array must not contain infs or NaNs\": \"unexpected Inf or NaN\",\n",
    "    \"contains NaN\": \"unexpected Inf or NaN\",\n",
    "    \"cannot convert float NaN to integer\": \"unexpected Inf or NaN\",\n",
    "    \"Error(s) in loading state_dict\": \"Model loading error\",\n",
    "    \"EOFError\": \"Model loading error\",\n",
    "    \"Restoring from checkpoint failed\": \"Model loading error\",\n",
    "    \"RecursionError: maximum recursion depth exceeded in comparison\": \"Max recursion depth exceeded\",\n",
    "    \"but PCA is expecting\": \"BROKEN Exathlon DATASETS\",  # ValueError: X has 44 features, but PCA is expecting 43 features as input.\n",
    "    \"input.size(-1) must be equal to input_size\": \"BROKEN Exathlon DATASETS\",\n",
    "    \"ValueError: The condensed distance matrix must contain only finite values.\": \"LinAlgError\",\n",
    "    \"LinAlgError\": \"LinAlgError\",\n",
    "    \"NameError: name 'nan' is not defined\": \"Not converged\",\n",
    "    \"Could not form valid cluster separation\": \"Not converged\",\n",
    "    \"contamination must be in\": \"Invariance/assumption not met\",\n",
    "    \"Data must not be constant\": \"Invariance/assumption not met\",\n",
    "    \"Cannot compute initial seasonals using heuristic method with less than two full seasonal cycles in the data\": \"Invariance/assumption not met\",\n",
    "    \"ValueError: Anom detection needs at least 2 periods worth of data\": \"Invariance/assumption not met\",\n",
    "    \"`dataset` input should have multiple elements\": \"Invariance/assumption not met\",\n",
    "    \"Cannot take a larger sample than population\": \"Invariance/assumption not met\",\n",
    "    \"num_samples should be a positive integer value\": \"Invariance/assumption not met\",\n",
    "    \"Cannot use heuristic method to compute initial seasonal and levels with less than periods + 10 datapoints\": \"Invariance/assumption not met\",\n",
    "    \"ValueError: The window size must be less than or equal to 0\": \"Invariance/assumption not met\",\n",
    "    \"The window size must be less than or equal to\": \"Incompatible parameters\",\n",
    "    \"window_size has to be greater\": \"Incompatible parameters\",\n",
    "    \"Set a higher piecewise_median_period_weeks\": \"Incompatible parameters\",\n",
    "    \"OutOfBoundsDatetime: cannot convert input with unit 'm'\": \"Incompatible parameters\",\n",
    "    \"`window_size` must be at least 4\": \"Incompatible parameters\",\n",
    "    \"elements of 'k' must be between\": \"Incompatible parameters\",\n",
    "    \"Expected n_neighbors <= n_samples\": \"Incompatible parameters\",\n",
    "    \"PAA size can't be greater than the timeseries size\": \"Incompatible parameters\",\n",
    "    \"All window sizes must be greater than or equal to\": \"Incompatible parameters\",\n",
    "    \"ValueError: __len__() should return >= 0\": \"Bug\",\n",
    "    \"stack expects a non-empty TensorList\": \"Bug\",\n",
    "    \"expected non-empty vector\": \"Bug\",\n",
    "    \"Found array with 0 feature(s)\": \"Bug\",\n",
    "    \"ValueError: On entry to DLASCL parameter number 4 had an illegal value\": \"Bug\",\n",
    "    \"Sample larger than population or is negative\": \"Bug\",\n",
    "    \"ZeroDivisionError\": \"Bug\",\n",
    "    \"IndexError\": \"Bug\",\n",
    "    \"status code '139'\": \"Bug\",\n",
    "    \"replacement has length zero\": \"Bug\",\n",
    "    \"missing value where TRUE/FALSE needed\": \"Bug\",\n",
    "    \"invalid subscript type 'list'\": \"Bug\",\n",
    "    \"subscript out of bounds\": \"Bug\",\n",
    "    \"invalid argument to unary operator\": \"Bug\",\n",
    "    \"negative length vectors are not allowed\": \"Bug\",\n",
    "    \"negative dimensions are not allowed\": \"Bug\",\n",
    "    \"`std` must be positive\": \"Bug\",\n",
    "    \"does not have key\": \"Bug\",  # State '1' does not have key '1'\n",
    "    \"Less than 2 uniques breaks left\": \"Bug\",\n",
    "    \"The encoder for value is invalid\": \"Bug\",\n",
    "    \"arange: cannot compute length\": \"Bug\",\n",
    "    \"n_components=3 must be between 0 and min(n_samples, n_features)\": \"Bug\",\n",
    "}\n",
    "\n",
    "def get_folder(index):\n",
    "    series = df.loc[index]\n",
    "    path = (\n",
    "        result_path /\n",
    "        series[\"algorithm\"] /\n",
    "        series[\"hyper_params_id\"] /\n",
    "        series[\"collection\"] /\n",
    "        series[\"dataset\"] /\n",
    "        str(series[\"repetition\"])\n",
    "    )\n",
    "    return path\n",
    "    \n",
    "def category_from_logfile(logfile):\n",
    "    with logfile.open() as fh:\n",
    "        log = fh.read()\n",
    "    for error in error_mapping:\n",
    "        if error in log:\n",
    "            return error_mapping[error]\n",
    "    #print(log)\n",
    "    return \"other\"\n",
    "    \n",
    "def extract_category(series):\n",
    "    status = series[\"status\"]\n",
    "    msg = series[\"error_message\"]\n",
    "    if status == \"Status.OK\":\n",
    "        return ok\n",
    "    elif status == \"Status.TIMEOUT\":\n",
    "        return timeout\n",
    "    # status is ERROR:\n",
    "    elif \"DockerAlgorithmFailedError\" in msg:\n",
    "        path = get_folder(series.name) / \"execution.log\"\n",
    "        if path.exists():\n",
    "            return category_from_logfile(path)\n",
    "        return \"DockerAlgorithmFailedError\"\n",
    "    else:\n",
    "        m = re.search(\"^([\\w]+)\\(.*\\)\", msg)\n",
    "        if m:\n",
    "            error = m.group(1)\n",
    "        else:\n",
    "            error = msg\n",
    "        return f\"TimeEval:{error}\"\n",
    "\n",
    "df[\"error_category\"] = df.apply(extract_category, axis=\"columns\", raw=False)\n",
    "df_error_category_overview = df.pivot_table(index=\"error_category\", columns=\"algorithm\", values=\"repetition\", aggfunc=\"count\")\n",
    "df_error_category_overview.insert(0, \"ALL (sum)\", df_error_category_overview.sum(axis=1))\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_error_category_overview.style.format(\"{:.0f}\", na_rep=\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>collection</th>\n",
       "      <th>dataset</th>\n",
       "      <th>algo_training_type</th>\n",
       "      <th>algo_input_dimensionality</th>\n",
       "      <th>dataset_training_type</th>\n",
       "      <th>dataset_input_dimensionality</th>\n",
       "      <th>train_preprocess_time</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_preprocess_time</th>\n",
       "      <th>...</th>\n",
       "      <th>error_message</th>\n",
       "      <th>repetition</th>\n",
       "      <th>hyper_params</th>\n",
       "      <th>hyper_params_id</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>RANGE_PR_AUC</th>\n",
       "      <th>AVERAGE_PRECISION</th>\n",
       "      <th>overall_time</th>\n",
       "      <th>error_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33500</th>\n",
       "      <td>TARZAN</td>\n",
       "      <td>Keogh</td>\n",
       "      <td>qtdbSel100MLII</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>KilledWorker('TARZAN-Keogh-qtdbSel100MLII-2de8...</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"alphabet_size\": 4, \"anomaly_window_size\": 20...</td>\n",
       "      <td>2de8bcd2ea03f0647d6008f4dc6c79d4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TimeEval:KilledWorker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33854</th>\n",
       "      <td>Telemanom</td>\n",
       "      <td>Keogh</td>\n",
       "      <td>qtdbSel100MLII</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>MULTIVARIATE</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>KilledWorker('Telemanom-Keogh-qtdbSel100MLII-d...</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"batch_size\": 64, \"dropout\": 0.5, \"early_stop...</td>\n",
       "      <td>d5b6a765591ff0db43c6d6369cb85f98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TimeEval:KilledWorker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34250</th>\n",
       "      <td>Torsk</td>\n",
       "      <td>Keogh</td>\n",
       "      <td>e0509m_rand_50</td>\n",
       "      <td>UNSUPERVISED</td>\n",
       "      <td>MULTIVARIATE</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>KilledWorker('Torsk-Keogh-e0509m_rand_50-de9b8...</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"context_window_size\": 10, \"density\": 0.01, \"...</td>\n",
       "      <td>de9b8b973f50539dc5ef4c8c1a722f4b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TimeEval:KilledWorker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36585</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>Keogh</td>\n",
       "      <td>e0509m_rand_50</td>\n",
       "      <td>UNSUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>SEMI_SUPERVISED</td>\n",
       "      <td>UNIVARIATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>KilledWorker('VALMOD-Keogh-e0509m_rand_50-f74f...</td>\n",
       "      <td>1</td>\n",
       "      <td>{\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...</td>\n",
       "      <td>f74fae266c1a6921831b154bf3f27c7f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TimeEval:KilledWorker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       algorithm collection         dataset algo_training_type  \\\n",
       "33500     TARZAN      Keogh  qtdbSel100MLII    SEMI_SUPERVISED   \n",
       "33854  Telemanom      Keogh  qtdbSel100MLII    SEMI_SUPERVISED   \n",
       "34250      Torsk      Keogh  e0509m_rand_50       UNSUPERVISED   \n",
       "36585     VALMOD      Keogh  e0509m_rand_50       UNSUPERVISED   \n",
       "\n",
       "      algo_input_dimensionality dataset_training_type  \\\n",
       "33500                UNIVARIATE       SEMI_SUPERVISED   \n",
       "33854              MULTIVARIATE       SEMI_SUPERVISED   \n",
       "34250              MULTIVARIATE       SEMI_SUPERVISED   \n",
       "36585                UNIVARIATE       SEMI_SUPERVISED   \n",
       "\n",
       "      dataset_input_dimensionality  train_preprocess_time  train_main_time  \\\n",
       "33500                   UNIVARIATE                    NaN              NaN   \n",
       "33854                   UNIVARIATE                    NaN              NaN   \n",
       "34250                   UNIVARIATE                    NaN              NaN   \n",
       "36585                   UNIVARIATE                    NaN              NaN   \n",
       "\n",
       "       execute_preprocess_time  ...  \\\n",
       "33500                      NaN  ...   \n",
       "33854                      NaN  ...   \n",
       "34250                      NaN  ...   \n",
       "36585                      NaN  ...   \n",
       "\n",
       "                                           error_message  repetition  \\\n",
       "33500  KilledWorker('TARZAN-Keogh-qtdbSel100MLII-2de8...           1   \n",
       "33854  KilledWorker('Telemanom-Keogh-qtdbSel100MLII-d...           1   \n",
       "34250  KilledWorker('Torsk-Keogh-e0509m_rand_50-de9b8...           1   \n",
       "36585  KilledWorker('VALMOD-Keogh-e0509m_rand_50-f74f...           1   \n",
       "\n",
       "                                            hyper_params  \\\n",
       "33500  {\"alphabet_size\": 4, \"anomaly_window_size\": 20...   \n",
       "33854  {\"batch_size\": 64, \"dropout\": 0.5, \"early_stop...   \n",
       "34250  {\"context_window_size\": 10, \"density\": 0.01, \"...   \n",
       "36585  {\"exclusion_zone\": 0.5, \"heap_size\": 50, \"max_...   \n",
       "\n",
       "                        hyper_params_id  ROC_AUC PR_AUC RANGE_PR_AUC  \\\n",
       "33500  2de8bcd2ea03f0647d6008f4dc6c79d4      NaN    NaN          NaN   \n",
       "33854  d5b6a765591ff0db43c6d6369cb85f98      NaN    NaN          NaN   \n",
       "34250  de9b8b973f50539dc5ef4c8c1a722f4b      NaN    NaN          NaN   \n",
       "36585  f74fae266c1a6921831b154bf3f27c7f      NaN    NaN          NaN   \n",
       "\n",
       "       AVERAGE_PRECISION  overall_time         error_category  \n",
       "33500                NaN           0.0  TimeEval:KilledWorker  \n",
       "33854                NaN           0.0  TimeEval:KilledWorker  \n",
       "34250                NaN           0.0  TimeEval:KilledWorker  \n",
       "36585                NaN           0.0  TimeEval:KilledWorker  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\n",
    "    (df[\"error_category\"] == \"TimeEval:KilledWorker\") &\n",
    "    (df[\"algorithm\"] != \"normal\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\n",
    "    #(df[\"error_category\"] == \"TimeEval:KilledWorker\") &\n",
    "    (df[\"algorithm\"] == \"VALMOD\")\n",
    "][[\"algorithm\", \"collection\", \"dataset\", \"status\", \"error_message\", \"error_category\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a labelling problem with the dataset `\"KDD-TSAD\", \"108_UCR_Anomaly_NOISEresperation2\"`.\n",
    "The raw dataset file specifies an anomaly-location of $168250$ until $168250$.\n",
    "Our preprocessing interprets those boundaries as ($[168250, 168250)$: begin incluse, end exclusive) and does not add an anomaly label (anomaly window size is $0$).\n",
    "This logic keeps the anomaly window sizes for other datasets at good-looking sizes, e.g. an anomaly-location $[7000, 7050)$ will result in a window size of $50$ (and not $51$, when considering the right bound ($7050$) inclusive).\n",
    "However, this (and at least two other) dataset will have no anomaly then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plot_scores(\"Series2Graph\", (\"KDD-TSAD\", \"108_UCR_Anomaly_NOISEresperation2\"))"
   ]
  }
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